Time-series-based prediction is an important area of focus in numerous applications. Time-series based prediction means predicting a type of information in the future, using historical values of the same type of information. Time-series-based prediction goes by many names and covers an enormous range of applications. Some common application areas include: financial prediction (e.g. predicting the value of a stock in the future based on the history and current value of the stock), traffic prediction e.g. (predicting the traffic speed in the future on a road segment based on the current and historical speeds on that road segment), retail sales prediction (e.g. predicting the amount of retail sales for a chain of stores given their current and historical sales levels), and many more.
For example, accurate short-term forecasting of traffic variables is essential for intelligent transportation systems applications, such as real-time route guidance and advanced traveler information systems. Hence, numerous modeling approaches have been proposed, including both nonparametric and parametric models.
Traffic forecasting models are usually evaluated on data from arterials and freeways, which are admittedly less variable than data from urban networks and not subject to the effects of traffic lights. In urban networks, neighborhood relationships and the definitions of spatial weight matrices for space-time parametric frameworks, are not straightforward; some locations may not be clearly upstream or downstream a given location. Furthermore, detectors can be dense in an urban network, so that locations with useful predictive information may be hard to identify; this again affects the construction of spatial weight matrices used in space-time modeling schemes. Erroneous and missing data are expected to be more frequent in urban networks, which makes essential the implementation of robust estimation procedures.
In order to achieve an acceptably good level of prediction accuracy on urban occupancy data, a new method needs to be developed.